Writing Data Frame / Plotting Functions

Wednesday, May 15

Today we will…

Discuss Groupt Project + Group Contract
New Material
- Calling Functions on Datasets
- Thinking About Missing Data
Lab 7: Functions and Fish

Group Project Details

Check out the Canvas page outlining the group project!

Groups have been assigned.
Your group contract is due on Monday!

Calling Functions on Datasets

Last Time…

We wrote a function called find_car_make() that takes in the name of a car and returns the “make” of the car (the company that created it).

find_car_make("Toyota Camry") returns “Toyota”.
find_car_make("Ford Anglica") returns “Ford”.

find_car_make <- function(car_name){
  make <- str_extract(string = car_name, 
                      pattern = "[:alpha:]*")
  return(make)
}

Pair Our Function with `dplyr`

Consider the mtcars data.

data(mtcars)
head(mtcars, n = 3)

               mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4     21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710    22.8   4  108  93 3.85 2.320 18.61  1  1    4    1

. . .

Let’s use our new function:

mtcars |> 
  rownames_to_column("make_model") |> 
  mutate(make = find_car_make(make_model),
         .after = make_model) |> 
  head(n = 3)

     make_model   make  mpg cyl disp  hp drat    wt  qsec vs am gear carb
1     Mazda RX4  Mazda 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
2 Mazda RX4 Wag  Mazda 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
3    Datsun 710 Datsun 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1

Recall the `penguins` Data

library(palmerpenguins)
data(penguins)
penguins |> 
  head()

# A tibble: 6 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           39.5          17.4               186        3800
3 Adelie  Torgersen           40.3          18                 195        3250
4 Adelie  Torgersen           NA            NA                  NA          NA
5 Adelie  Torgersen           36.7          19.3               193        3450
6 Adelie  Torgersen           39.3          20.6               190        3650
# ℹ 2 more variables: sex <fct>, year <int>

Function to Standardize Data

We want to take in a vector of numbers and standardize it – make all values be between 0 and 1.

. . .

std_to_01 <- function(var) {
  stopifnot(is.numeric(var))
  
  num <- var - min(var, na.rm = TRUE)
  denom <- max(var, na.rm = TRUE) - min(var, na.rm = TRUE)
  
  return(num / denom)
}

Standardizing Variables

Is it a good idea to standardize (scale) variables in a data analysis?

Why standardize?

Easier to compare across variables.
Easier to model – standardizes the amount of variability.

Why not standardize?

More difficult to interpret the values.

. . .

E.g., a penguin with a bill length of 35 mm (std to 0.11) and a mass of 5500 g (std to 0.78).

Pair Our Function with `dplyr`

Let’s standardize penguin measurements.

penguins |> 
  mutate(bill_length_mm    = std_to_01(bill_length_mm), 
         bill_depth_mm     = std_to_01(bill_depth_mm), 
         flipper_length_mm = std_to_01(flipper_length_mm), 
         body_mass_g       = std_to_01(body_mass_g))

Ugh. Still copy-pasting!

. . .

Recall across()!

penguins |> 
  mutate(across(.cols = bill_length_mm:body_mass_g,
                .fns = ~ std_to_01(.x))) |> 
  slice_head(n = 4)

# A tibble: 4 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>   <fct>              <dbl>         <dbl>             <dbl>       <dbl>
1 Adelie  Torgersen          0.255         0.667             0.153       0.292
2 Adelie  Torgersen          0.269         0.512             0.237       0.306
3 Adelie  Torgersen          0.298         0.583             0.390       0.153
4 Adelie  Torgersen         NA            NA                NA          NA    
# ℹ 2 more variables: sex <fct>, year <int>

Use variables as function arguments?

std_column_01 <- function(data, variable) {
  stopifnot(is.data.frame(data))
  
  data <- data |> 
    mutate(variable = std_to_01(variable))
  return(data)
}

Note

I used the existing function std_to_01() inside the new function for clarity!

. . .

But it didn’t work…

std_column_01(penguins, body_mass_g)

Error in `mutate()`:
ℹ In argument: `variable = std_to_01(variable)`.
Caused by error:
! object 'body_mass_g' not found

Tidy Evaluation

Functions using unquoted variable names as arguments are said to use nonstandard evaluation or tidy evaluation.

Tidy:

penguins |> 
  pull(body_mass_g)

penguins$body_mass_g

Untidy:

penguins[, "body_mass_g"]

penguins[["body_mass_g"]]

. . .

Tidy evaluation isn’t naturally supported when writing your own functions.

Defused R Code

When a piece of code is defused, R doesn’t return its value like normal.

Instead it returns an expression that describes how to evaluate it.

. . .

Evaluated code:

1 + 1

[1] 2

Defused code:

expr(1 + 1)

1 + 1

. . .

We produce defused code when we use tidy evaluation and our own functions don’t know how to handle it.

Solution 1

Don’t use tidy evaluation in your own functions.

This is more complicated to read and use, but it’s safe.

std_column_01 <- function(data, variable) {
  stopifnot(is.data.frame(data))
  
  data[[variable]] <- std_to_01(data[[variable]])
  return(data)
}

std_column_01(penguins, "bill_length_mm")

Solution 2: `rlang`

Use the rlang package!

This package provides operators that simplify writing functions around tidyverse pipelines.

knitr::include_graphics("https://github.com/rstudio/hex-stickers/blob/main/thumbs/rlang.png?raw=true")

Read more about using this package for function writing here!

Solution 2: `rlang`

Two ways to get around the issue of defused code:

Embrace Operator ({ })

With { }, you can transport a variable from one function to another.

. . .

Defuse and Inject

You can first use enquo(arg) to defuse the variable.
Then use !!arg to inject the variable.

Solution 2: `rlang`

If we use either of these solutions, we also need to use the walrus operator (:=).

This means we have to use := instead of = in any dplyr verb containing one of these rlang fixes.

Recall Our Broken Function

std_column_01 <- function(data, variable) {
  stopifnot(is.data.frame(data))
  
  data <- data |> 
    mutate(variable = std_to_01(variable))
  return(data)
}

std_column_01(penguins, body_mass_g)

Error in `mutate()`:
ℹ In argument: `variable = std_to_01(variable)`.
Caused by error:
! object 'body_mass_g' not found

The code is defused, so mutate() doesn’t know what body_mass_g is.
We need to modify variable to make this work correctly!

std_column_01 <- function(data, variable) {
  stopifnot(is.data.frame(data))

  data <- data |>
    mutate({{variable}} := std_to_01({{variable}}))
  return(data)
}

std_column_01(penguins, body_mass_g)

# A tibble: 6 × 7
  species island    bill_length_mm bill_depth_mm body_mass_g sex     year
  <fct>   <fct>              <dbl>         <dbl>       <dbl> <fct>  <int>
1 Adelie  Torgersen           39.1          18.7       0.292 male    2007
2 Adelie  Torgersen           39.5          17.4       0.306 female  2007
3 Adelie  Torgersen           40.3          18         0.153 female  2007
4 Adelie  Torgersen           NA            NA        NA     <NA>    2007
5 Adelie  Torgersen           36.7          19.3       0.208 female  2007
6 Adelie  Torgersen           39.3          20.6       0.264 male    2007

std_column_01 <- function(data, variable) {
  stopifnot(is.data.frame(data))
  
  variable <- enquo(variable)

  data <- data |>
    mutate(!!variable := std_to_01(!!variable))
  return(data)
}

std_column_01(penguins, body_mass_g)

# A tibble: 6 × 7
  species island    bill_length_mm bill_depth_mm body_mass_g sex     year
  <fct>   <fct>              <dbl>         <dbl>       <dbl> <fct>  <int>
1 Adelie  Torgersen           39.1          18.7       0.292 male    2007
2 Adelie  Torgersen           39.5          17.4       0.306 female  2007
3 Adelie  Torgersen           40.3          18         0.153 female  2007
4 Adelie  Torgersen           NA            NA        NA     <NA>    2007
5 Adelie  Torgersen           36.7          19.3       0.208 female  2007
6 Adelie  Torgersen           39.3          20.6       0.264 male    2007

Inject Multiple Variables

What if I want to modify multiple columns?

Use across()!

std_column_01 <- function(data, variables) {
  stopifnot(is.data.frame(data))
  
  data <- data |> 
    mutate(across(.cols = {{variables}},
                  .fns = ~ std_to_01(.x)))
  return(data)
}

std_column_01(penguins, bill_length_mm:body_mass_g)

# A tibble: 5 × 7
  species island    bill_length_mm bill_depth_mm body_mass_g sex     year
  <fct>   <fct>              <dbl>         <dbl>       <dbl> <fct>  <int>
1 Adelie  Torgersen          0.255         0.667       0.292 male    2007
2 Adelie  Torgersen          0.269         0.512       0.306 female  2007
3 Adelie  Torgersen          0.298         0.583       0.153 female  2007
4 Adelie  Torgersen         NA            NA          NA     <NA>    2007
5 Adelie  Torgersen          0.167         0.738       0.208 female  2007

Missing Data

Types of Missing Data

Missing Completely at Random (MCAR)
- No difference between missing and observed values.
- Missing observations are a random subset of all observations.
Missing at Random (MAR)
- Systematic difference between missing and observed values, but can be entirely explained by other observed variables.
Missing Not at Random (MNAR)
- Missingness is directly related to the unobserved value.

Types of Missing Data

Consider a study of depression.

Missing Completely at Random (MCAR)
- Some subjects have missing lab values because a batch of samples was processed improperly.
Missing at Random (MAR)
- Subjects who identify as men are less likely to complete a survey on depression severity.
Missing Not at Random (MNAR)
- Subjects with more severe depression are less likely to complete a survey on depression severity.

When we remove missing data…

We implicitly assume observations are missing completely at random!

We might be mostly removing data from subjects who identify as men.
We might be mostly removing data from subjects with severe depression.
We are inadvertently making our data less representative.

. . .

We need to take more care when dealing with missing values!

Dealing with Missing Data

Look for patterns!
- Do observations with missing values have similar traits?

. . .

Consider outside explanations!
- Why might missing data exist?
- Should we have a “missing” category in our analysis?

. . .

Can we impute values?
- If depression is MCAR within gender, age, and education level, then the distribution of depression will be similar for people of the same gender, age, and education level.

Wednesday, May 15

Group Project Details

Calling Functions on Datasets

Last Time…

Pair Our Function with dplyr

Recall the penguins Data

Function to Standardize Data

Standardizing Variables

Pair Our Function with dplyr

Use variables as function arguments?

Tidy Evaluation

Defused R Code

Solution 1

Solution 2: rlang

Solution 2: rlang

Solution 2: rlang

Recall Our Broken Function

Fixing Our Broken Function

Inject Multiple Variables

Missing Data

Types of Missing Data

Types of Missing Data

When we remove missing data…

Dealing with Missing Data

Pair Our Function with `dplyr`

Recall the `penguins` Data

Pair Our Function with `dplyr`

Solution 2: `rlang`

Solution 2: `rlang`

Solution 2: `rlang`